This Page Has Moved: Late Chronotypes and Early Classes

Late chronotypes and early classes

Researchers: Andrew SID Lang, Philip P Nelson, Moses Satralkar, Ailin Li, Claire E Ferguson, Laura A Kaneta

Data Collection and Curation

We used a standard MEQ (Morningness-Eveningness Questionnaire) instrument [1] programmed into a Google Form to collect responses from several first semester freshmen level courses (what were they?) during the first few weeks of class. This resulted in a dataset of 650 responses.

We curated our data by removing all non-freshmen, all entries not aged 17-19, and several duplicates (student who were in more than one course that we surveyed).

When we wrote the questionnaire, we left the answers free response. This resulted in some non-standard responses for questions 11, 12, and 19. We conjecture that a few students who felt a little torn between two adjoining categories entered a value between the two standard responses. We ended up curating several zeros to ones for question 19 but decided to leave the rest of the data as originally entered: question 11 has 5 ones, 13 threes, and 15 fives; question 12 had 24 ones and 13 fours; and question 19 had 47 threes and 9 fives.

Once the semester had ended we collected the grades of these students and worked out their overall semester GPA and their GPAs for hourly bins corresponding to class start times: 7:00-7:59 AM, 8:00-8:59 AM, 9:00-9:59 AM, 10:00-10:59 AM, 11:00-11:59 AM, 12:00-12:59 PM, 1:00-1:59 PM, 2:00-2:59 PM, 3:00-3:59 PM, 4:00-4:59 PM, 5:00-5:59 PM, and 6:00-6:59 PM. We did not include grades from classes from which the student withdrew and for classes that had different start times on different days we took the time from the day with the longest class period.

This left a final data file with 402 unique records that is ready for modeling 17-19 yrs Freshmen Only.

Data Analysis

Our dataset consists of MEQ Scores and first-semester GPAs by class starting time of 402 first-time college freshmen aged 17-19. Scores can range from 16-86; however our scores range from 17-68 with the following distribution between types [1]:

Type	Range	N	%	Female	Male
definite evening	16-30	12	3%	7	5
moderate evening	31-41	95	24%	65	30
intermediate	42-58	258	64%	171	87
moderate morning	59-69	37	9%	22	15
definite morning	70-86	0	0%	0	0

GPA vs Chronotype


The trend line shows the evening types obtain lower grades compared to morning types.

GPA vs Chronotype by Gender


The trend lines show that the effect is more significant for males than females.

#R Code
library(ggplot2) #graphics library
setwd("C://...")
mydata = read.csv(file="20180327 17-19 yrs Ready for Analysis.csv",header=TRUE,row.names="id")
summary(mydata)
 
ggplot(mydata, aes(x=Total, y=GPA))
+ geom_point(color='#2980B9', size = 4)
+ geom_smooth(method=lm, color='#2C3E50') #plotting the data
 
GPAlmAll <- lm(GPA ~ Total + Sex + US.Resident + College, data=mydata)
summary (GPAlmAll)
[output]
Call:
lm(formula = GPA ~ Total + Sex + US.Resident + College, data = mydata)
 
Residuals:
    Min      1Q  Median      3Q     Max
-2.8381 -0.3453  0.1963  0.5524  1.1150
 
Coefficients:
                                Estimate Std. Error t value Pr(>|t|)
(Intercept)                     2.784120   0.254341  10.946  < 2e-16 ***
Total                           0.012216   0.004294   2.845  0.00468 **
Sex                            -0.129858   0.083112  -1.562  0.11899
US.Resident                    -0.105936   0.143670  -0.737  0.46134
CollegeBusiness                -0.007007   0.124435  -0.056  0.95513
CollegeEducation                0.103880   0.153129   0.678  0.49793
CollegeNursing                  0.110656   0.132337   0.836  0.40357
CollegeScience and Engineering -0.139757   0.103054  -1.356  0.17583
CollegeTheology and Ministry    0.123386   0.146178   0.844  0.39914
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 
Residual standard error: 0.7471 on 393 degrees of freedom
Multiple R-squared:  0.04753,   Adjusted R-squared:  0.02814
F-statistic: 2.451 on 8 and 393 DF,  p-value: 0.01342
[output]
 
GPAlmGender <- lm(GPA ~ Total + Sex, data=mydata)
summary(GPAlmGender)
[output]
Call:
lm(formula = GPA ~ Total + Sex, data = mydata)
 
Residuals:
    Min      1Q  Median      3Q     Max
-2.9894 -0.3603  0.2018  0.5513  1.0066
 
Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  2.680268   0.205459  13.045  < 2e-16 ***
Total        0.012391   0.004282   2.894  0.00402 **
Sex         -0.170118   0.078754  -2.160  0.03136 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 
Residual standard error: 0.748 on 399 degrees of freedom
Multiple R-squared:  0.03072,   Adjusted R-squared:  0.02586
F-statistic: 6.323 on 2 and 399 DF,  p-value: 0.001979
[output]
 
confint(GPAlmGender, level=0.95) # CIs for model parameters
[output]
                   2.5 %      97.5 %
(Intercept)  2.276350120  3.08418664
Total        0.003972952  0.02080941
Sex         -0.324942073 -0.01529337
[output]
 
GPAlm <- lm(GPA ~ Total, data=mydata)
summary(GPAlm)
[output]
Call:
lm(formula = GPA ~ Total, data = mydata)
 
Residuals:
    Min      1Q  Median      3Q     Max
-2.9293 -0.3643  0.1778  0.5857  1.0130
 
Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.636441   0.205390  12.836  < 2e-16 ***
Total       0.012090   0.004299   2.812  0.00517 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 
Residual standard error: 0.7514 on 400 degrees of freedom
Multiple R-squared:  0.01939,   Adjusted R-squared:  0.01693
F-statistic: 7.908 on 1 and 400 DF,  p-value: 0.005165
[output]
 
#Now do just GPA vs. Total Score for all times.
lm <- lm(X7am ~ Total + Sex, data=mydata)
summary(lm)
confint(lm, level=0.95) # CIs for model parameters
[output]
 Call:
lm(formula = X7am ~ Total + Sex, data = mydata)
 
Residuals:
    Min      1Q  Median      3Q     Max
-3.1128 -0.3263  0.5305  0.7239  1.0268
 
Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  2.54713    0.52732   4.830 4.36e-06 ***
Total        0.01551    0.01077   1.440    0.153
Sex         -0.07029    0.19281  -0.365    0.716
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 
Residual standard error: 1.005 on 112 degrees of freedom
  (287 observations deleted due to missingness)
Multiple R-squared:  0.01849,   Adjusted R-squared:  0.0009604
F-statistic: 1.055 on 2 and 112 DF,  p-value: 0.3517
 
                   2.5 %     97.5 %
(Intercept)  1.502308773 3.59195336
Total       -0.005830429 0.03685108
Sex         -0.452315382 0.31174105
[output]
#...

GPA when controlling for gender

Time	N	Slope	95% CI	p-value
7am	113	0.01551	-0.005830429 - 0.03685108	0.153
8am	224	0.017807	0.001105433 - 0.03450847	0.0368
9am	325	0.011562	-0.001864789 - 0.02498809	0.0912
10am	235	0.013259	0.0008523036 - 0.02566505	0.0363
11am	18	0.01928	-0.01463043 - 0.05318793	0.2468
12pm	239	0.008412	-0.005886458 - 0.02271084	0.248
1pm	272	-0.002481	-0.01588811 - 0.0109260	0.716
2pm	326	0.011813	0.001633317 - 0.02199244	0.0231
3pm	238	0.017493	0.003886605 - 0.03110015	0.012
4pm	46	0.006865	-0.02266914 - 0.03639984	0.642
5pm	31	-0.003902	-0.03647586 - 0.02867283	0.808
6pm	39	0.004018	-0.0275687 - 0.03560398	0.798187

The model slopes (size of effect of MEQ score on GPA controlled by Gender) by class start time were analysed. The color is size of confidence interval and the label is the number of data points used to create the slope values.



The results trend line:

Panes		Line		Coefficients
Row	Column	p-value	DF	Term	Value	StdErr	t-value	p-value
Slope	Time	0.0379781	10	Time	-0.0012825	0.0005367	-2.3897	0.0379781
				intercept	0.026001	0.0069596	3.73598	0.0038721

This shows that MEQ scores are more significant for early course than for later ones.

More Analysis
The data was split by MEQ score into the top and bottom 20%, leaving 60% in the middle. Then average GPA by class starting time was analysed for each group.



The model results are as follows (the red color indicates less than 50 data values):
Individual trend lines:

Trend Line Coefficients:

Row Column	p-value	DF	Term	Value	StdErr	t-value	p-value
GPA Bottom 20%	0.0512913	9	Time	0.0486018	0.0216339	2.24655	0.0512913
intercept				2.611	0.284101	9.19038	< 0.0001
GPA Middle 60%	0.001727	9	Time	0.0420615	0.0095652	4.39734	0.001727
intercept				2.89689	0.125612	23.0621	< 0.0001
GPA Top 20%	0.476167	9	Time	0.0159039	0.021392	0.743452	0.476167
intercept				3.16824	0.280923	11.2779	< 0.0001

Chronotype and Time Period
The data was subsetted in morning (7,8, and 9), middle-of-the-day (11, 12, 13, 14, and 15), and afternoon (16, 17, and18) classes. Then we used R to find the relationship between GPA and Chronotype for each subset.

library(Publish)
 
setwd("...")
 
mydata = read.csv(file="20180405GPAByTimeOfDayWithChronotypeWithTimeType.csv",
header=TRUE,row.names="id")
 
A1 <- subset(mydata,TimePeriod=="Morning" & Chronotype=="definite evening")
A2 <- subset(mydata,TimePeriod=="Morning" & Chronotype=="intermediate")
A3 <- subset(mydata,TimePeriod=="Morning" & Chronotype=="moderate evening")
A4 <- subset(mydata,TimePeriod=="Morning" & Chronotype=="moderate morning")
B1 <- subset(mydata,TimePeriod=="Middle of the Day" & Chronotype=="definite evening")
B2 <- subset(mydata,TimePeriod=="Middle of the Day" & Chronotype=="intermediate")
B3 <- subset(mydata,TimePeriod=="Middle of the Day" & Chronotype=="moderate evening")
B4 <- subset(mydata,TimePeriod=="Middle of the Day" & Chronotype=="moderate morning")
C1 <- subset(mydata,TimePeriod=="Afternoon" & Chronotype=="definite evening")
C2 <- subset(mydata,TimePeriod=="Afternoon" & Chronotype=="intermediate")
C3 <- subset(mydata,TimePeriod=="Afternoon" & Chronotype=="moderate evening")
C4 <- subset(mydata,TimePeriod=="Afternoon" & Chronotype=="moderate morning")
 
ci.mean(A1$GPA)
ci.mean(A2$GPA)
ci.mean(A3$GPA)
ci.mean(A4$GPA)
ci.mean(B1$GPA)
ci.mean(B2$GPA)
ci.mean(B3$GPA)
ci.mean(B4$GPA)
ci.mean(C1$GPA)
ci.mean(C2$GPA)
ci.mean(C3$GPA)
ci.mean(C4$GPA)
 
[output]
> ci.mean(A1$GPA)
 mean CI-95%
 2.56 [1.74;3.39]
> ci.mean(A2$GPA)
 mean CI-95%
 3.19 [3.10;3.29]
> ci.mean(A3$GPA)
 mean CI-95%
 2.94 [2.76;3.12]
> ci.mean(A4$GPA)
 mean CI-95%
 3.43 [3.25;3.60]
> ci.mean(B1$GPA)
 mean CI-95%
 3.19 [2.86;3.51]
> ci.mean(B2$GPA)
 mean CI-95%
 3.38 [3.31;3.44]
> ci.mean(B3$GPA)
 mean CI-95%
 3.20 [3.07;3.32]
> ci.mean(B4$GPA)
 mean CI-95%
 3.49 [3.35;3.63]
> ci.mean(C1$GPA)
 mean CI-95%
 3.15 [2.44;3.86]
> ci.mean(C2$GPA)
 mean CI-95%
 3.56 [3.45;3.66]
> ci.mean(C3$GPA)
 mean CI-95%
 3.36 [3.12;3.60]
> ci.mean(C4$GPA)
 mean CI-95%
 3.63 [3.40;3.87]
[output]

The results show a typical increase of GPA for all chronotypes as the day goes on but the rate of increase is, as expected, dependent on chronotype.

References

1. Terman M, Terman JS. Light therapy for seasonal and nonseasonal depression: efficacy, protocol, safety, and side
effects. CNS Spectrums, 2005;10:647-663. (Downloadable at www.cet.org)